






'''simple docstring'''





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


_UpperCamelCase						:						Union[str, Any]  =					['gpt2']
_UpperCamelCase						:						Optional[int]  =					'gpt2'

if is_tf_available():


						class  snake_case__      (					tf.Module):




											def __init__(  self  :		Optional[int]				, _A  :		List[Any]      )    ->							str:
														super().__init__()
														UpperCAmelCase_      :       List[str]          =       tokenizer
														UpperCAmelCase_      :       Tuple          =       AutoConfig.from_pretrained(_A      )
														UpperCAmelCase_      :       Dict          =       TFGPTaLMHeadModel.from_config(_A      )





											@tf.function(input_signature=(tf.TensorSpec((None,)				, tf.string				, name='''text'''      ),)      )
											def 		A      (  self  :		Dict				, _A  :		int      )    ->							Union[str, Any]:
														UpperCAmelCase_      :       Optional[Any]          =       self.tokenizer(_A      )
														UpperCAmelCase_      :       Optional[int]          =       tokenized['''input_ids'''].to_tensor()

														UpperCAmelCase_      :       Union[str, Any]          =       tf.cast(input_ids_dense > 0				, tf.intaa      )
														# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

														UpperCAmelCase_      :       Any          =       self.model(input_ids=_A				, attention_mask=_A      )['''logits''']

														return outputs


@require_tf
@require_keras_nlp
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Any      )    ->							List[str]:
								super().setUp()

								UpperCAmelCase_      :       Optional[int]          =       [GPTaTokenizer.from_pretrained(_A      ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
								UpperCAmelCase_      :       Union[str, Any]          =       [TFGPTaTokenizer.from_pretrained(_A      ) for checkpoint in TOKENIZER_CHECKPOINTS]
								assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

								UpperCAmelCase_      :       str          =       [
								    '''This is a straightforward English test sentence.''',
								    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
								    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
								    '''And some much more rare Chinese: 齉 堃 齉堃''',
								    '''Je vais aussi écrire en français pour tester les accents''',
								    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
								]
								UpperCAmelCase_      :       int          =       list(zip(self.test_sentences				, self.test_sentences[::-1]      )      )




					def 		A      (  self  :		Optional[int]      )    ->							Dict:
								for tokenizer, tf_tokenizer in zip(self.tokenizers				, self.tf_tokenizers      ):
											for test_inputs in self.test_sentences:
														UpperCAmelCase_      :       int          =       tokenizer([test_inputs]				, return_tensors='''tf'''      )
														UpperCAmelCase_      :       int          =       tf_tokenizer([test_inputs]      )

														for key in python_outputs.keys():
																	# convert them to numpy to avoid messing with ragged tensors
																	UpperCAmelCase_      :       int          =       python_outputs[key].numpy()
																	UpperCAmelCase_      :       Dict          =       tf_outputs[key].numpy()

																	self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape      )      )
																	self.assertTrue(tf.reduce_all(tf.cast(_A				, tf.intaa      ) == tf_outputs_values      )      )




					@slow
					def 		A      (  self  :		Tuple      )    ->							Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase_      :       Dict          =       tf.function(_A      )
											for test_inputs in self.test_sentences:
														UpperCAmelCase_      :       str          =       tf.constant(_A      )
														UpperCAmelCase_      :       Tuple          =       compiled_tokenizer(_A      )
														UpperCAmelCase_      :       List[Any]          =       tf_tokenizer(_A      )

														for key in eager_outputs.keys():
																	self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )




					@slow
					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase_      :       List[Any]          =       ModelToSave(tokenizer=_A      )
											UpperCAmelCase_      :       List[Any]          =       tf.convert_to_tensor([self.test_sentences[0]]      )
											UpperCAmelCase_      :       Dict          =       model.serving(_A      )  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
														UpperCAmelCase_      :       Tuple          =       Path(_A      ) / '''saved.model'''
														tf.saved_model.save(_A				, _A				, signatures={'''serving_default''': model.serving}      )
														UpperCAmelCase_      :       Optional[int]          =       tf.saved_model.load(_A      )
											UpperCAmelCase_      :       int          =       loaded_model.signatures['''serving_default'''](_A      )['''output_0''']
											# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
											self.assertTrue(tf.reduce_all(out == loaded_output      )      )




					@slow
					def 		A      (  self  :		List[str]      )    ->							Tuple:
								for tf_tokenizer in self.tf_tokenizers:
											UpperCAmelCase_      :       Union[str, Any]          =       tf.convert_to_tensor([self.test_sentences[0]]      )
											UpperCAmelCase_      :       List[str]          =       tf_tokenizer(_A      )  # Build model with some sample inputs

											UpperCAmelCase_      :       List[Any]          =       tf_tokenizer.get_config()
											UpperCAmelCase_      :       Union[str, Any]          =       TFGPTaTokenizer.from_config(_A      )
											UpperCAmelCase_      :       Any          =       model_from_config(_A      )

											for key in from_config_output.keys():
														self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]      )      )





					@slow
					def 		A      (  self  :		Any      )    ->							List[Any]:
								for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											UpperCAmelCase_      :       Union[str, Any]          =       12_31_23

											for max_length in [3, 5, 10_24]:
														UpperCAmelCase_      :       Union[str, Any]          =       tf.convert_to_tensor([self.test_sentences[0]]      )
														UpperCAmelCase_      :       Dict          =       tf_tokenizer(_A				, max_length=_A      )

														UpperCAmelCase_      :       Optional[int]          =       out['''input_ids'''].numpy().shape[1]

														assert out_length == max_length










'''simple docstring'''





import argparse
import os
import re

import packaging.version


_UpperCamelCase						:						Union[str, Any]  =					'examples/'
_UpperCamelCase						:						List[str]  =					{
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
_UpperCamelCase						:						Optional[Any]  =					{
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
_UpperCamelCase						:						Any  =					'README.md'
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			List[Any]			,       A					:			Tuple		)    ->   Dict:
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       int          =       f.read()
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       REPLACE_PATTERNS[pattern]
			UpperCAmelCase_      :       Tuple          =       replace.replace('''VERSION'''			,       A		)
			UpperCAmelCase_      :       Tuple          =       re_pattern.sub(A			,       A		)
			with open(A			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						f.write(A		)
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Any:
			for folder, directories, fnames in os.walk(A		):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove('''research_projects'''		)
						if "legacy" in directories:
									directories.remove('''legacy'''		)
						for fname in fnames:
									if fname.endswith('''.py'''		):
												update_version_in_file(os.path.join(A			,       A		)			,       A			,       pattern='''examples'''		)
def __UpperCAmelCase   (					A					:			Tuple			,       A					:			Optional[int]=False		)    ->   Tuple:
			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(A			,       A			,       A		)
			if not patch:
						update_version_in_examples(A		)
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       List[str]          =       '''🤗 Transformers currently provides the following architectures'''
			UpperCAmelCase_      :       int          =       '''1. Want to contribute a new model?'''
			with open(A			,       '''r'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						UpperCAmelCase_      :       Optional[int]          =       f.readlines()

			# Find the start of the list.
			UpperCAmelCase_      :       List[str]          =       0
			while not lines[start_index].startswith(_start_prompt		):
						start_index += 1
			start_index += 1

			UpperCAmelCase_      :       Any          =       start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt		):
						if lines[index].startswith('''1.'''		):
									UpperCAmelCase_      :       Optional[Any]          =       lines[index].replace(
									    '''https://huggingface.co/docs/diffusers/main/model_doc'''			,       '''https://huggingface.co/docs/diffusers/model_doc'''			,       )
						index += 1

			with open(A			,       '''w'''			,       encoding='''utf-8'''			,       newline='''\n'''		) as f:
						f.writelines(A		)
def __UpperCAmelCase   (					)    ->   Dict:
			with open(REPLACE_FILES['''init''']			,       '''r'''		) as f:
						UpperCAmelCase_      :       Any          =       f.read()
			UpperCAmelCase_      :       Union[str, Any]          =       REPLACE_PATTERNS['''init'''][0].search(A		).groups()[0]
			return packaging.version.parse(A		)
def __UpperCAmelCase   (					A					:			str=False		)    ->   List[Any]:
			UpperCAmelCase_      :       Union[str, Any]          =       get_version()
			if patch and default_version.is_devrelease:
						raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''		)
			if default_version.is_devrelease:
						UpperCAmelCase_      :       Optional[int]          =       default_version.base_version
			elif patch:
						UpperCAmelCase_      :       Any          =       F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
			else:
						UpperCAmelCase_      :       Optional[Any]          =       F"{default_version.major}.{default_version.minor + 1}.0"

			# Now let's ask nicely if that's the right one.
			UpperCAmelCase_      :       List[str]          =       input(F"Which version are you releasing? [{default_version}]"		)
			if len(A		) == 0:
						UpperCAmelCase_      :       Tuple          =       default_version

			print(F"Updating version to {version}."		)
			global_version_update(A			,       patch=A		)
def __UpperCAmelCase   (					)    ->   Optional[int]:
			UpperCAmelCase_      :       str          =       get_version()
			UpperCAmelCase_      :       Tuple          =       F"{current_version.major}.{current_version.minor + 1}.0.dev0"
			UpperCAmelCase_      :       Any          =       current_version.base_version

			# Check with the user we got that right.
			UpperCAmelCase_      :       List[Any]          =       input(F"Which version are we developing now? [{dev_version}]"		)
			if len(A		) == 0:
						UpperCAmelCase_      :       Optional[int]          =       dev_version

			print(F"Updating version to {version}."		)
			global_version_update(A		)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
						_UpperCamelCase						:						Tuple  =					argparse.ArgumentParser()
						parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
						parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
						_UpperCamelCase						:						Optional[Any]  =					parser.parse_args()
						if not args.post_release:
												pre_release_work(patch=args.patch)
						elif args.patch:
												print('Nothing to do after a patch :-)')
						else:
												post_release_work()


